Volume-Scaled Common Nearest Neighbor Clustering Algorithm with Free-Energy Hierarchy

TL;DR

This paper presents the volume-scaled common nearest neighbor clustering algorithm, which incorporates free-energy hierarchy to improve clustering of molecular dynamics data within Markov state models.

Contribution

It introduces a novel density-based clustering method that directly links to free-energy landscapes, enabling hierarchical clustering of complex molecular conformations.

Findings

The vs-CNN algorithm effectively captures conformational states.

It provides a hierarchical scheme for free-energy landscape analysis.

The method enhances clustering accuracy for MD simulation data.

Abstract

The combination of Markov state modeling (MSM) and molecular dynamics (MD) simulations has been shown in recent years to be a valuable approach to unravel the slow processes of molecular systems with increasing complexity. While the algorithms for intermediate steps in the MSM workflow like featurization and dimensionality reduction have been specifically adapted for MD data sets, conventional clustering methods are generally applied for the discretization step. This work adds to recent efforts to develop specialized density-based clustering algorithms for the Boltzmann-weighted data from MD simulations. We introduce the volume-scaled common nearest neighbor (vs-CNN) clustering that is an adapted version of the common nearest neighbor (CNN) algorithm. A major advantage of the proposed algorithm is that the introduced density-based criterion directly links to a free-energy notion via Boltzmann inversion. Such a free-energy perspective allows for a straightforward hierarchical scheme to identify conformational clusters at different levels of a generally rugged free-energy landscape of complex molecular systems.